Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	32,061
2	Florida	29,380
3	Mississippi	28,084
4	Arizona	27,803
5	Alabama	26,027
6	Georgia	24,162
7	South Carolina	23,276
8	Nevada	22,631
9	New York	22,630
10	Tennessee	22,444
11	Texas	22,262
12	New Jersey	21,849
13	Iowa	20,869
14	Rhode Island	20,769
15	Arkansas	20,378
16	District of Columbia	19,906
17	Illinois	18,869
18	Massachusetts	18,699
19	Idaho	18,299
20	California	18,136
21	Maryland	18,087
22	Delaware	18,007
23	Nebraska	17,873
24	Utah	16,377
25	North Carolina	16,205
26	North Dakota	15,751
27	South Dakota	15,541
28	Oklahoma	15,011
29	Kansas	14,928
30	Connecticut	14,867
31	Indiana	14,406
32	Virginia	14,248
33	Missouri	14,222
34	Wisconsin	14,029
35	Minnesota	13,547
36	New Mexico	12,142
37	Kentucky	11,585
38	Michigan	11,411
39	Pennsylvania	10,903
40	Ohio	10,660
41	Puerto Rico	10,464
42	Washington	10,251
43	Colorado	10,092
44	Alaska	8,350
45	Montana	7,041
46	Wyoming	6,679
47	Oregon	6,391
48	Hawaii	6,093
49	West Virginia	5,862
50	New Hampshire	5,366
51	Maine	3,383
52	Vermont	2,620

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Dakota	304
2	Alabama	253
3	North Dakota	225
4	Iowa	212
5	Tennessee	193
6	Louisiana	189
7	Florida	186
8	Missouri	184
9	Kansas	181
10	Mississippi	174
11	Oklahoma	172
12	South Carolina	168
13	Idaho	154
14	Georgia	152
15	Nebraska	141
16	North Carolina	140
17	Illinois	139
18	Kentucky	137
19	Texas	134
20	Minnesota	124
21	Arkansas	123
22	Indiana	122
23	Nevada	122
24	Hawaii	120
25	California	119
26	Virginia	109
27	Wisconsin	109
28	Utah	105
29	Rhode Island	100
30	West Virginia	100
31	Ohio	93
32	Puerto Rico	90
33	Maryland	86
34	Montana	84
35	Delaware	63
36	Michigan	62
37	District of Columbia	58
38	Alaska	56
39	Washington	53
40	Oregon	52
41	Pennsylvania	52
42	Arizona	48
43	Connecticut	47
44	Wyoming	47
45	Colorado	45
46	New Mexico	44
47	Massachusetts	41
48	New Jersey	36
49	New York	36
50	Vermont	16
51	Maine	14
52	New Hampshire	12

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,795
2	New York	1,673
3	Massachusetts	1,315
4	Connecticut	1,252
5	Louisiana	1,072
6	Rhode Island	991
7	District of Columbia	860
8	Mississippi	837
9	Arizona	693
10	Michigan	677
11	Illinois	654
12	Maryland	622
13	Delaware	621
14	Pennsylvania	606
15	South Carolina	535
16	Florida	529
17	Georgia	527
18	Indiana	491
19	Alabama	448
20	Texas	448
21	Nevada	426
22	New Mexico	375
23	Iowa	356
24	Ohio	356
25	Colorado	339
26	California	332
27	Minnesota	331
28	New Hampshire	317
29	Virginia	306
30	Arkansas	269
31	Missouri	269
32	Washington	265
33	North Carolina	264
34	Tennessee	256
35	Kentucky	220
36	Nebraska	209
37	Idaho	207
38	Oklahoma	204
39	North Dakota	195
40	Wisconsin	195
41	South Dakota	188
42	Kansas	155
43	Puerto Rico	136
44	Utah	127
45	West Virginia	123
46	Oregon	110
47	Maine	98
48	Montana	98
49	Vermont	92
50	Wyoming	70
51	Hawaii	51
52	Alaska	49

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Mississippi	7
2	Louisiana	5
3	Arkansas	4
4	Florida	4
5	Georgia	4
6	Alabama	3
7	South Carolina	3
8	Texas	3
9	California	2
10	Hawaii	2
11	Idaho	2
12	Kentucky	2
13	Missouri	2
14	New Mexico	2
15	Tennessee	2
16	Wyoming	2
17	Arizona	1
18	Illinois	1
19	Indiana	1
20	Iowa	1
21	Massachusetts	1
22	Nevada	1
23	North Carolina	1
24	North Dakota	1
25	Ohio	1
26	Oklahoma	1
27	Rhode Island	1
28	Virginia	1
29	Washington	1
30	West Virginia	1
31	Alaska	0
32	Colorado	0
33	Connecticut	0
34	Delaware	0
35	District of Columbia	0
36	Kansas	0
37	Maine	0
38	Maryland	0
39	Michigan	0
40	Minnesota	0
41	Montana	0
42	Nebraska	0
43	New Hampshire	0
44	New Jersey	0
45	New York	0
46	Oregon	0
47	Pennsylvania	0
48	Puerto Rico	0
49	South Dakota	0
50	Utah	0
51	Vermont	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	143,389	1	99
Lafayette	Florida	142,959	2	99
Lincoln	Arkansas	123,464	3	99
Lake	Tennessee	119,726	4	99
Lee	Arkansas	113,357	5	99
Davidson	Tennessee	37,484	164	94
Richland	South Carolina	27,331	358	88
Orange	California	15,375	1014	67
York	South Carolina	15,311	1019	67
Pierce	Washington	8,349	1861	40

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,612	1	99
Galax city	Virginia	4,412	2	99
Randolph	Georgia	3,983	3	99
Terrell	Georgia	3,634	4	99
Neshoba	Mississippi	3,434	5	99
Richland	South Carolina	471	689	78
Davidson	Tennessee	373	890	71
Orange	California	311	1046	66
Pierce	Washington	207	1395	55
York	South Carolina	167	1563	50

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.